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Predicting drug responsiveness in humans cancers using genetically engineered mice

ABSTRACT: Anti-cancer drug testing is challenging, but genetically engineered mouse models (GEMMs) and orthotopic, syngeneic transplants (OSTs) may offer advantages for pre-clinical testing including an intact microenvironment. We examined the efficacy of six chemotherapeutic or targeted anti-cancer drugs, alone and in combination, using over 500 GEMMs/OSTs representing three distinct breast cancer subtypes: Basal-like (C3(1)-T-antigen GEMM), Luminal B (MMTV-Neu GEMM), and Claudin-low (T11/TP53-/- OST). While a few single agents offered exceptional efficacy like lapatinib in the Neu/ERBB2 driven model, combination therapies tended to be more active and life prolonging. Using expression profiling of chemotherapy treated murine tumors, we identified an expression signature that was able to predict pathological complete response to neoadjuvant anthracycline-taxane treated human breast cancer patients, even after accounting for the common clinical variables and other genomic signatures. These results show that credentialed murine models can predict the efficacy of would-be anti-cancer compounds in humans, and that GEMMs can be used to develop new biomarkers of therapeutic responsiveness in humans.

ORGANISM(S): Mus musculus

PROVIDER: GSE37915 | GEO | 2014/01/01

SECONDARY ACCESSION(S): PRJNA166913

REPOSITORIES: GEO

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Predicting drug responsiveness in humans cancers using genetically engineered mice

Project description:Anti-cancer drug testing is challenging, but genetically engineered mouse models (GEMMs) and orthotopic, syngeneic transplants (OSTs) may offer advantages for pre-clinical testing including an intact microenvironment. We examined the efficacy of six chemotherapeutic or targeted anti-cancer drugs, alone and in combination, using over 500 GEMMs/OSTs representing three distinct breast cancer subtypes: Basal-like (C3(1)-T-antigen GEMM), Luminal B (MMTV-Neu GEMM), and Claudin-low (T11/TP53-/- OST). While a few single agents offered exceptional efficacy like lapatinib in the Neu/ERBB2 driven model, combination therapies tended to be more active and life prolonging. Using expression profiling of chemotherapy treated murine tumors, we identified an expression signature that was able to predict pathological complete response to neoadjuvant anthracycline-taxane treated human breast cancer patients, even after accounting for the common clinical variables and other genomic signatures. These results show that credentialed murine models can predict the efficacy of would-be anti-cancer compounds in humans, and that GEMMs can be used to develop new biomarkers of therapeutic responsiveness in humans. control X treatment

2014-01-01 | E-GEOD-37915 | biostudies-arrayexpress

Combined PI3K/mTOR and MEK Inhibition Provides Broad Anti-Tumor Activity in Faithful Murine Cancer Models

Project description:Anticancer drug development is an inefficient process, with potential therapeutics demonstrating a high attrition rate due to lack of efficacy in Phase II/III testing. In an effort to develop improved pre-clinical predictors of efficacy, we and others have turned to testing in genetically engineered murine models (GEMMs) of cancer, which may offer some advantages to in vitro and xenograft systems. Specifically, we assessed the activity of 16 treatment regimens in a Ras-driven, Ink4a/Arf-deficient melanoma GEMM. Like human RAS-mutant melanoma, this GEMM was refractory to standard chemotherapy and single-agent small molecule therapies. Only one regimen exhibited significant anti-tumor activity in this model: combined treatment with AZD6244 (MEK inhibitor) and BEZ235 (dual PI3K/mTOR inhibitor), which produced marked tumor regression and improved survival. Given the surprising activity of the “AZD/BEZ” combination in a melanoma GEMM, we next tested this regimen in a Ras-driven orthotopic-transplant model of “claudin-low” breast cancer, which shares some gene expression features with melanoma. The AZD/BEZ regimen also exhibited significant activity in this related Ras-driven model, leading us to testing in even more diverse GEMMs of basal-like and luminal breast cancer. The AZD/BEZ combination was highly active in each of these distinct breast models, demonstrating equal or greater efficacy compared to any other regimen tested in studies of over 700 tumor-bearing mice. This regimen even exhibited activity in tumors selected for resistance to another effective chemotherapy agent, lapatinib, in HER2+ models. These results demonstrate the utility of credentialed murine models for large-scale efficacy testing of diverse anti-cancer regimens, and predict combinations of PI3K/mTOR and MEK inhibitors will demonstrate anti-tumor activity in a wide-range of human malignancies. 16 array samples

2012-08-21 | E-GEOD-35722 | biostudies-arrayexpress

Combined PI3K/mTOR and MEK Inhibition Provides Broad Anti-Tumor Activity in Faithful Murine Cancer Models

2012-08-21 | GSE35722 | GEO

GEMM CRC collection analysis

Project description:A collection of genetically engineered mouse models (GEMM) of colorectal cancer (CRC) were created, and primary tumors from these GEMMs were analyzed. Primary CRC tumors from these GEMMs were genotyped to confirm that they contain the core genetic lesions of interest, including APC, P53, KRAS, and BRAF. Primary tumors from GEMMs with combinations of lesions of interest were analyzed by whole genome expression, and their expression profiles were compared to determine how they segregate. Signatures were then generated from GEMM tumors of interest and compared to human clinical datasets with expression and outcome data. Primary tumors from CRC GEMMs with different combinations of mutant alleles of interested were generated and analyzed. Alleles include mutant forms of APC (A), P53 (P), KRAS (K) and BRAF (B).

2014-07-01 | E-GEOD-50794 | biostudies-arrayexpress

GEMM CRC collection analysis

2014-07-01 | GSE50794 | GEO

Drug perturbation RNA sequencing of 2 prostate cancer cell lines

Project description:Prioritizing cancer treatments at the individual patient level remains challenging, and performing co-clinical studies using patient-derived models in real-time is often not unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework to predict and validate drug sensitivity sensitivity in both a human tumors and in its their pre-existing high est-fidelity (cognate) (cognate) model(s)—for contextual in vivo validation—) by leveraging perturbational profiles of clinically-relevant oncology drugs. As proof-of-concept, we applied OncoLoop to prostate cancer (PCa) using a series of genetically engineered mouse models (GEMMs) that capture the broad spectrum of disease states, including metastatic, castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of published cohorts revealed that most patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT), based on upon Master Regulator (MR) conservation analysis. Drugs recurrently predicted to recurrently invert MR protein activity in patients and their cognate GEMM-DTs were successfully validated, including in two cognate allografts and one cognate patient derived xenograft (PDX). OncoLoop is highly generalizable and can be extended to other cancers and potentially other pathologdiseasesies.

2022-10-31 | GSE199800 | GEO

Distinct mesenchymal cell states mediate prostate cancer progression (human)

Project description:We sought to determine whether molecular alterations in tumor stroma influence prostate cancer progression and metastatic potential. To accomplish this, we compared mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for both common and GEMM-specific transcriptional programs. These are conserved between mouse models and human prostate cancers with the same genomic drivers. The transcriptional profiles of the stroma of murine models of advanced disease were similar to those of human prostate cancer bone metastases, with periostin expression by stromal cells influencing invasion and neuroendocrine differentiation. These profiles were then used to build a robust gene signature that can predict metastatic progression in localized disease independent of Gleason score. Taken together, this offers new evidence that the stromal microenvironment mediates prostate cancer progression.

2023-10-10 | GSE244267 | GEO

Distinct mesenchymal cell states mediate prostate cancer progression [Spatial Transcriptomics]

2023-12-01 | GSE248466 | GEO

Distinct mesenchymal cell states mediate prostate cancer progression (mouse)

2023-10-10 | GSE244269 | GEO

Cross-species analysis of genome-wide regulatory networks identifies a synergistic dependency between FOXM1 and CENPF that drives prostate cancer malignancy

Project description:Analysis of the transcriptome of mouse models of prostate cancer to assemble a mouse prostate cancer interactome. To assemble the mouse prostate cancer interactome, we collected 13 distinct mice or genetically-engineered mouse models (GEMMs), which together represent the full spectrum of prostate cancer phenotypes including: normal epithelium (i.e., wild-type), low-grade PIN (i.e., Nkx3.1 and APT), high-grade PIN and adenocarcinoma (i.e., APT-P; APC; Myc; NP; Erg-P; and NP53), castration-resistant prostate cancer (i.e., NP-AI), and metastatic prostate cancer (i.e., NPB; NPK; and TRAMP). To further enhance the heterogeneity afforded by this diversity of mouse models, we pharmacologically perturbed each GEMM using 13 different drugs (or appropriate vehicle). The resulting mouse prostate tissue/tumor dataset encompassed 384 expression profiles Total RNA obtained from prostate tumors/tissues of 13 mouse models of prostate cancer treated with 13 different drugs for 5 consecutive days. Prostate tumors/tissues were harvested and processed for RNA isolation and transcriptome analysis.

2014-05-12 | E-GEOD-53202 | biostudies-arrayexpress

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